Noise reduction in radiation image

ABSTRACT

Among other things, one or more techniques and/or systems are described for processing images yielded from an examination via radiation to reduce visible noise in the images. After an image is reconstructed, a noise contribution to the image (e.g., an amount of noise in the image) is estimated to determine a target noise contribution for the image. The target noise contribution for the image may vary based upon, among other things, dose of radiation, aspects or properties of an object being imaged, etc. The image is subsequently filtered using one or more filtering techniques to generate a filtered image, and a noise contribution to the filtered image is determined. When the noise contribution to the filtered image satisfies the target noise contribution (e.g., a sufficient amount of noise has been filtered out of the image), the filtered image is combined with the reconstructed image to generate a blended image.

BACKGROUND

The present application relates to reducing noise in imagesreconstructed from data acquired during an examination of an object viaionizing radiation, such as x-rays or gamma rays. It finds particularapplication in medical environments where dosage to a patient ismonitored, however it may also find applicability in security,industrial, and/or other applications where noise reduction inreconstructed images is desirable.

Today, radiation imaging systems such as computed tomography (CT)systems, single-photon emission computed tomography (SPECT) systems,digital projection systems, and/or line-scan systems, for example, areuseful to provide information, or images, of interior aspects of anobject under examination. The object is exposed to rays of radiationphotons (e.g., x-ray photons, gamma ray photons, etc.) and radiationphotons traversing the object are detected by a detector arraypositioned substantially diametrically opposite a radiation sourcerelative to the object. A degree to which the radiation photons areattenuated by the object (e.g., absorbed, reflected, etc.) is measuredto determine one or more properties of the object, or rather aspects ofthe object. For example, highly dense aspects of the object typicallyattenuate more radiation than less dense aspects, and thus an aspecthaving a higher density, such as a bone or metal, for example, may beapparent when surrounded by less dense aspects, such as tissue orclothing.

Noise is inherently introduced into the system when measuring orsampling photons or rather when measuring/sampling charge generated fromphotons impinging the detector array. This noise is sometimes referredto as photon noise, and artifacts (e.g., streaking, blurring, etc.) inimages generated from an examination are sometimes attributable, atleast in part, to this photon noise. Accordingly, the photon noise mayreduce the quality of an image.

Due to quantum statistics, the photon noise level (e.g., ratio of photonnoise to useful signal) generated from an examination of an object isinversely related to the dose of radiation applied to the object. Forexample, the photon noise level increases as the dose applied to theobject decreases. Accordingly, in some applications, the dose ofradiation applied to the object is balanced with the desire for imageshaving few to no artifacts.

SUMMARY

Aspects of the present application address the above matters, andothers. According to an aspect, a method for processing images yieldedfrom an examination via radiation is provided. The method comprisesreceiving an image of an object that has been exposed to radiation,where the image is generated based upon an interaction between theradiation and the object. The method also comprises estimating a firstnoise contribution to the image to derive a target noise contributionand filtering the image to generate a first filtered image. The methodfurther comprises estimating a second noise contribution to the firstfiltered image and comparing the target noise contribution to the secondnoise contribution to determine whether the target noise contributionhas been satisfied by the filtering.

According to another aspect, a computer-readable medium comprisingprocessor-executable instructions that when executed perform a method isprovided. The method comprises estimating a first noise contribution toan image yielded from a radiation examination of an object to derive atarget noise contribution and filtering the image to generate a firstfiltered image. The method also comprises estimating a second noisecontribution to the first filtered image and comparing the target noisecontribution to the second noise contribution to determine whether thetarget noise contribution has been satisfied by the filtering. Themethod further comprises combining the first filtered image with theimage when, responsive to the comparing, the target noise contributionhas been satisfied by the filtering.

According to yet another aspect a radiation imaging system is provided.The radiation imaging system includes a radiation source configured toexpose an object under examination to radiation and a detector arrayconfigured to produce one or more signals based upon detecting at leastsome of the radiation that traverses the object. The system alsocomprises an image reconstruction component configured to reconstruct animage based upon the one or more signals and a noise reductioncomponent. The noise reduction component is configured to estimate afirst noise contribution to the image to derive a target noisecontribution, filter the image to generate a first filtered image, andestimate a second noise contribution to the first filtered image. Thenoise reduction component is also configured to compare the target noisecontribution to the second noise contribution to determine whether thetarget noise contribution has been satisfied by the filtering andcombine the first filtered image with the image when, responsive to thecomparing, the target noise contribution has been satisfied by thefiltering.

Those of ordinary skill in the art will appreciate still other aspectsof the present application upon reading and understanding the appendeddescription.

FIGURES

The application is illustrated by way of example and not limitation inthe figures of the accompanying drawings, in which like referencesgenerally indicate similar elements and in which:

FIG. 1 illustrates an example environment of a radiation imaging system.

FIG. 2 is a flow diagram illustrating an example method for processingimages yielded from an examination via radiation.

FIG. 3 is a flow diagram illustrating an example method for processingimages yielded from an examination via radiation.

FIG. 4 is an illustration of an example computer-readable mediumcomprising processor-executable instructions configured to embody one ormore of the provisions set forth herein.

DESCRIPTION

The claimed subject matter is now described with reference to thedrawings, wherein like reference numerals are generally used to refer tolike elements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding of the claimed subject matter. It may beevident, however, that the claimed subject matter may be practicedwithout these specific details. In other instances, structures anddevices are illustrated in block diagram form in order to facilitatedescribing the claimed subject matter.

According to some embodiments, systems and/or techniques for reducingartifacts, caused by photon noise, in post-reconstruction images areprovided. An image, which may be a two-dimensional image or athree-dimensional image is received and the amount of noise, or a firstnoise contribution, in the image is estimated. Based upon thisestimation, a desired noise target, or target noise contribution, forthe image is computed. For example, it may be desirable to remove 20% ofthe noise from the image, such that the image is merely 80% ‘noisy’. Oneor more noise reduction filters are then applied to the image and/or torespective pixels thereof to reduce the noise and/or to more evenlydistribute the noise amongst pixels. The resulting image may be referredto as a filtered image, and an amount of noise, or a second noisecontribution, in the filtered image is estimated using one or more noiseestimation techniques. The estimation of the noise in the filteredimage, or the second noise contribution, is compared to the desirednoise target, or the target noise contribution, to determine if thedesired noise target has been satisfied by the filtering (e.g., has thefiltering sufficiently reduced the noise in the image).

If the desired noise target has not been satisfied, at least some of theone or more noise reduction filters are applied to the filtered image togenerate a second filtered image. The amount of noise in this secondfiltered image, or a third noise contribution, is estimated, and theestimation of the noise in the second filtered image is compared to thedesired noise target to determine if the desired noise target has beensatisfied (e.g., is the image now sufficiently less noisy). Such aprocess may be iteratively repeated until the desired noise target hasbeen satisfied and/or until some other stopping criteria has beensatisfied (e.g., the process has been repeated at least a specifiednumber of times).

When the desired noise target has been satisfied and/or the otherstopping criteria has been satisfied, the most recently generatedfiltered image is blended with the original image (e.g., thepost-reconstruction image) to generate a blended image. For example, avalue of a first pixel of the original image is merged (e.g., averaged)with a value of a corresponding pixel of the most recently generatedfiltered image to generate a value for a first pixel of the blendedimage. In some embodiments, a contribution of the original image to theblended image is weighted equally with a contribution of the mostrecently generated filtered image to the blended image. In someembodiments, the contribution of the original image is weighteddifferently than the contribution of the most recently generatedfiltered image to the blended image. Accordingly, a value for a firstpixel of the blended image may be an average of a first weighted valueof the first pixel in the original image and a second weighted value ofthe first pixel in the most recently generated filtered image, where afirst weight is applied to a value of the first pixel in the originalimage to generate the first weighted value and a second weight isapplied to a value of the first pixel in the most recently generatedfiltered image to generate the second weighted value, where the firstweight may be the same as or different than the second weight.

FIG. 1 illustrates a radiation imaging system 100 where the techniquesand/or systems described herein may be employed. In the illustratedembodiment, the radiation imaging system 100 is a computed tomography(CT) system, although the systems and/or techniques described herein mayfind applicability to other radiation imaging systems such as line-scansystems, mammography systems, and/or diffraction systems, for example.The radiation imaging system 100 thus merely provides an examplearrangement and is not intended to be interpreted in a limiting manner,such as necessarily specifying the location, inclusion, and/or relativeposition of the components depicted therein. By way of example, in someembodiments, a data acquisition component 122 is part of a detectorarray 118 and/or is located on a rotating gantry 106 of an examinationunit 102.

In the example radiation imaging system 100, the examination unit 102 isconfigured to examine objects 104. The examination unit 102 comprisesthe rotating gantry 106 and a (stationary) support structure 108 (e.g.,which may encase and/or surround at least a portion of the rotatinggantry 106 (e.g., as illustrated with an outer, stationary ring,surrounding an outside edge of an inner, rotating ring)). During anexamination of an object 104, the object 104 is placed on a supportarticle 110, such as a bed or conveyor belt, for example, and positionedwithin an examination region 112 (e.g., a hollow bore in the rotatinggantry 106), where the object 104 is exposed to radiation 120.

The rotating gantry 106 may surround a portion of the examination region112 and may comprise a radiation source 116 (e.g., an ionizing radiationsource such as an x-ray source and/or gamma-ray source) and the detectorarray 118. The detector array 118 is typically mounted on asubstantially diametrically opposite side of the rotating gantry 106relative to the radiation source 116, and during an examination of theobject 104, the rotating gantry 106 (e.g., including the radiationsource 116 and detector array 118) is rotated about the object 104.Because the radiation source 116 and the detector array 118 are mountedto a same rotating gantry 106, a relative position between the detectorarray 118 and the radiation source 116 is substantially maintainedduring the rotation of the rotating gantry 106.

During the examination of the object 104, the radiation source 116 emitscone-beam and/or fan-beam radiation configurations from a focal spot ofthe radiation source 116 (e.g., a region within the radiation source 116from which radiation 120 emanates) into the examination region 112. Suchradiation 120 may be emitted substantially continuously and/or may beemitted intermittently (e.g., a brief pulse of radiation 120 is emittedfollowed by a resting period during which the radiation source 116 isnot activated). Further, the radiation 120 may be emitted at a singleenergy spectrum or multiple energy spectrums depending upon, among otherthings, whether the radiation imaging system 100 is configured as asingle-energy system or a multi-energy (e.g., dual-energy) system.

As the emitted radiation 120 traverses the object 104, the radiation 120may be attenuated differently by different aspects of the object 104.Because different aspects attenuate different percentages of theradiation 120, the number of photons and/or energy levels of respectivephotons detected by detector cells of the detector array 118 may vary.For example, more dense aspects of the object(s) 104, such as a bone,may attenuate more of the radiation 120 (e.g., causing fewer photons toimpinge upon a region of the detector array 118 shadowed by the moredense aspects) than less dense aspects, such as tissue.

Radiation detected by the detector array 118 may be directly convertedand/or indirectly converted into analog signals that can be transmittedfrom the detector array 118 to the data acquisition component 122operably coupled to the detector array 118. The analog signal(s) maycarry information indicative of the radiation detected by the detectorarray 118 (e.g., such as an amount of charge measured over a samplingperiod and/or an energy level of detected radiation) as well as photonnoise that, due to quantum statistics, is inherently associated withdetecting photons.

The data acquisition component 122 is configured to convert the analogsignals output by the detector array 118 into digital signals and/or tocompile signals that were transmitted within a predetermined timeinterval, or measurement interval, using various techniques (e.g.,integration, photon counting, etc.). The compiled signals are typicallyin projection space and are, at times, referred to as projections. Aprojection may be representative of the information collected ormeasurements acquired by respective detector cells of the detector array118 during an interval of time or a view, where a view corresponds todata collected while the radiation source 116 was at a particularview-angle or within a particular angular range relative to the object104.

The projections generated by the data acquisition component 122 may betransmitted to an image reconstruction component 124 operably coupled tothe data acquisition component 122. The image reconstruction component124 is configured to convert at least some of the data from projectionspace to image space using suitable analytical, iterative, and/or otherreconstruction techniques (e.g., tomosynthesis reconstruction,back-projection, iterative reconstruction, etc.). The images generatedby the image reconstruction component 124 (e.g., at times referred to aspost-reconstruction images) may be in two-dimensional space and/orthree-dimensional space and may be representative of the degree ofattenuation through various aspects of the object 104 for a given view,may be representative of the density of various aspects of the object104, and/or may be representative of the z-effective of various aspectsof the object 104, for example.

In some embodiments, due to among other things, the photon noiseinherently introduced into the system when detecting photons, at leastsome images generated by the image reconstruction component 124 maycomprise artifacts that blur, streak, etc. a portion of the image,conceal a portion of the object 104 within the image, and/or otherwisereduce the quality and/or diagnostic usefulness of the image.

Accordingly, at least some images generated by the image reconstructioncomponent 124 are transmitted to a noise reduction component 126configured to reduce the degree of visible noise within the image (e.g.,thus reducing artifacts in the image) by applying one or more filters tothe image to generate a filtered image. In some embodiments, thisfiltered image is blended with the image received from the imagereconstruction component 124 to generate a blended image, where pixelvalues in the blended image are yielded by merging pixel values of theimage received from the image reconstruction component 124 with pixelvalues of the filtered image. In some embodiments, by blending the imagereceived from the image reconstruction component 124 (e.g., at timesreferred to as an original image) with the filtered image, edges offeatures within the object are substantially maintained while thevisible noise in the image is substantially reduced relative to theoriginal image to improve image quality (e.g., by reducing artifacts)and/or to improve diagnostic usefulness, for example.

The example radiation imaging system 100 further comprises a terminal128, or workstation (e.g., a computer), that may be configured toreceive blended images output by the noise reduction component 126and/or to receive images output by the image reconstruction component124, which may be displayed on a monitor 130 to a user 132 (e.g.,security personnel, medical personnel, etc.). In this way, the user 132can inspect the image(s) to identify areas of interest within the object104, for example. The terminal 128 can also be configured to receiveuser input which can direct operations of the examination unit 102(e.g., a speed to rotate, a speed and direction of the support article110, etc.), for example.

In the example radiation imaging system 100, a controller 134 isoperably coupled to the terminal 128. The controller 134 may beconfigured to control operations of the examination unit 102, forexample. By way of example, in one embodiment, the controller 134 may beconfigured to receive information from the terminal 128 and to issueinstructions to the examination unit 102 indicative of the receivedinformation (e.g., change the position of the support article relativeto the radiation source 116, etc.).

Referring to FIG. 2, an example method 200 for post-reconstruction imagefiltering is provided. The method begins at 202 and an image of anobject is received at 204. The image is produced by exposing the objectto ionizing radiation, such as x-rays or gamma rays, andmeasuring/detecting x-rays that traverse the object. In someembodiments, the image is a two-dimensional image and respective pixelsrepresent a degree of attenuation through the object. In otherembodiments, the image is a three-dimensional image and respectivevoxels represent a density, z-effective, or other property of a portionof the object. For purposes of this application, unless otherwiseexplicitly noted, the term pixel is meant to refer to or comprise both atwo-dimensional pixel and a three-dimensional voxel.

In some embodiments, the image that is received at 202 comprisesartifacts and/or other features that reduce the quality of the imageand/or the diagnostic usefulness of the image by smearing or otherwisedistorting aspects of the object, concealing aspects of the object, etc.At least some of these artifacts may be attributable to noise in theimage, such as yielded from photon noise introduced during the detectionof radiation photons.

At 206 in the example method 200, a noise contribution to the image(e.g., at times referred to herein as a first noise contribution) isestimated to derive a target noise contribution. In some embodiments,the noise contribution to the image is estimated by computing thespatial derivative of respective pixels in one or more dimensions. Byway of example, where the image is a three-dimensional image, thespatial derivative of a value of the pixel (e.g., at times referred toas a CT number or a Hounsfield value of the pixel) in the x dimensionand in the y dimension is computed (e.g., where an x,y plane generallycorresponds to a plane in which the rotating gantry 106 is rotated). Insome embodiments, the median value of at least some of these spatialderivatives is subsequently computed and this median value is weightedwith respect to one or more reconstruction parameters (e.g., parametersdefined when reconstructing the image) to determine the noisecontribution to the image. In some embodiments, a robust medianestimator is used to estimate the noise in the image, where one or moreparameters of the robust median estimator are based upon (e.g., afunction of) parameters of an image reconstruction algorithm utilized toreconstruct the image.

In some embodiments, prior to computing the median value, pixels areclassified or grouped based upon the value of respective pixels. By wayof example, in an examination of a patient, pixels with a value in therange of −100 to 100 Hounsfield Units (HU) may be typicallyrepresentative of tissue (e.g., veins, organs, etc.) while pixels havinga value outside of this range may be typically representative ofnon-tissue (e.g., bones, air, implants, etc.). In some embodiment,merely the spatial derivatives of those pixels having a value in therange of −100 to 100 HU are considered when computing the median value.Accordingly, the spatial derivative of pixels having a value outside ofthe range, and thus likely to be representative of non-tissue, are notfactored in when estimating the noise in the image.

At 208 in the example method 200, the image is filtered to generate afirst filtered image. One or more filtering techniques may be applied tothe image to generate the filtered image. By way of example, in someembodiments, an outlier filter, such as a 3D outlier filter, is appliedto the image. The outlier filter is configured to compare a first pixelto a set of pixels neighboring the first pixel and to adjust one or moreproperties of the first pixel based upon properties of the set of pixelsneighboring the first pixel if the one or more properties of the firstpixel deviate substantially from the properties of the set of pixels.

As an example of a 3D outlier filter, respective pixels of the image maybe associated with a value, such as a CT value. During a 3D outlierfiltering process, a mean pixel value of a set of pixels neighboring afirst pixel may be computed. This mean pixel value may be non-weightedor weighted (e.g., where the values of pixels immediately adjacent thefirst pixel are assigned a weight that is different than a weight thatis assigned to the values of pixels that are separated from the firstpixel by at least one pixel). A value of the first pixel may be comparedto the mean pixel value to determine a deviation value for the firstpixel. If the deviation value exceeds a deviation threshold, the meanpixel value may be applied to the first pixel. In this way, the originalvalue of the first pixel (e.g., as computed during image reconstruction)is replaced with the mean pixel value of the set of pixels neighboringthe first pixel.

Such a process of comparing the value of a pixel to a mean pixel valuefor a set of pixels neighboring the pixel may be repeated for aplurality of pixels in the image.

In some embodiments, the deviation threshold is varied between pixels ofthe image. By way of example, the deviation threshold for a pixel may beset as a function of a value of the pixel when the image was received at204. By way of example, if the value of the pixel is in a pixel valuerange of −100 to 100 HU (e.g., and thus the pixel is likely to berepresentative of tissue), the deviation threshold may be set to a firstdeviation threshold (e.g., 1 sigma, such that a deviation value thatexceeds 1 sigma causes the mean pixel value, for a set of pixelsneighboring the pixel, to be applied to the pixel). If the value of thepixel is not in the pixel value range of −100 to 100 HU (e.g., and thusthe pixel is not likely to be representative of tissue), the deviationthreshold may be set to a second deviation threshold (e.g., 3 sigma,such that a deviation value that exceeds 3 sigma causes the mean pixelvalue, for a set of pixels neighboring the pixel, to be applied to thepixel). In this way, pixels representative of tissue, which may beexpected to have little to no deviation if no noise is present, aretreated differently than pixels representative of non-tissue, which maynaturally have greater deviation in the pixel values due to variationsin the density, z-effective, etc. of non-tissue features of an object,for example.

In may be appreciated that due to edges in an object (e.g., due to atransition from tissue to a bone, for example), at least some pixels mayhave a value that substantially differs from the values of neighboringpixels. Accordingly, in some embodiments, an upper deviation thresholdmay be defined as well. In such embodiments, the mean pixel value of aset of pixels neighboring a pixel may be applied to the pixel if thedeviation value for the first pixel is between the deviation thresholdand the upper deviation threshold. For example, the deviation thresholdand the upper deviation threshold may be set at 1 sigma and 3 sigma,respectively, for a pixel having a value in the range of −100 to 100 HU.If the deviation value of the pixel is less than 1 sigma or greater than3 sigma, the mean pixel value is not assigned to the pixel. If thedeviation value of the pixel is between 1 sigma and 3 sigma, the meanpixel value for the set of pixels neighboring the pixel is assigned tothe pixel to replace the original value of the pixel (e.g., as computedduring image reconstruction).

In some embodiments, a non-linear filter may be applied to the image inaddition to the outlier filter and/or instead of applying the outlierfilter. The non-linear filter is intended to preserve edges in the imagewhile filtering noise in the image by reducing and/or redistributing thenoise in the image. By way of example, the non-linear filter maycomprise a diffusion filter configured to compute the difference betweena value of a first pixel and a mean pixel value of pixels neighboringthe first pixel. In some embodiments, if the value of the first pixel isgreater than the mean pixel value, at least some of the difference isdistributed to the pixels neighboring the first pixel (e.g., causing thevalue of the first pixel to decrease while the respective values of oneor more pixels neighboring the first pixel increase). In this way, aportion of a signal represented by the first pixel of the image isdistributed to a set of one or more pixels neighboring the first pixel,for example.

In some embodiments, a degree of diffusion (e.g., a percentage of thedifference distributed and/or the number of pixels across which thedifference is distributed) is defined by a set of parameters. Further,as will be explained in more detail below, the degree of diffusion maychange over the course of several iterations (e.g., during a firstiteration, 4% of the difference is diffused, during a next iteration,another 3% of the original difference is diffused, etc.).

It may be appreciated that while the diffusion filtering technique isdescribed with respect to merely a first pixel, such a technique may beapplied to a plurality of pixels, such as respective pixels of theimage. Moreover, it may be appreciated that a diffusion filter is merelyone example type of non-linear filter and that other types of non-linearfilters are also contemplated. For example, in another embodiment, thenon-linear filter comprises a wavelet filter, which uses various filters(e.g., various stages of high pass filters, low pass filters, etc.) toprocess data associated with high spatial frequency samples differentlythan data associated with low spatial frequency samples.

At 210 in the example method 200, the noise contribution to the filteredimage (e.g., referred to herein at times as a second noise contribution)is estimated to approximate how much noise remains after the image hasbeen filtered to generate the filtered image. In some embodiments, thenoise contribution is estimated using a robust median estimator or othernoise estimation technique (e.g., as further described with respect toestimating the first noise contribution at 206).

At 212, the target noise contribution derived at 206 is compared to thesecond noise contribution estimated at 210 to determine whether thetarget noise contribution has been satisfied by the filtering. That is,the second noise contribution is compared to the target noisecontribution to determine whether a desired amount of noise has beenremoved from the image received at 204.

When, responsive to the comparing, the target noise contribution hasbeen satisfied, the first filtered image is combined (e.g., blended)with the image to generate a blended image at 212. The blended imagerepresents a blending of the pixels of the first filtered image with thepixels of the image received at 204. By way of example, in someembodiments, a value of a first pixel of the image received at 204 isaveraged with a value of a corresponding pixel of the first filteredimage to determine a value for a first pixel of the blended image.

In some embodiments, the first filtered image and the image received at204 are blended using a weighted average, where a percent contributionto the blended image by the first filtered image is the same as ordifferent than a percent contribution by the image received at 204. Byway of example, in some embodiments, the user may specify a desiredcontribution, or weight, of the first filtered image and/or the originalimage to the blended image. If the user specifies a 20% contribution bythe first filtered image, respective pixels of the blended image aredetermined by weighting the pixels of the image received at 204 by 80%to generate a first weighted image and weighting the pixels of the firstfiltered image by 20% to generate a second weighted image. The firstweighted image and the second weighted image may be subsequently mergedto generate the blended image. Accordingly, the value for the firstpixel of the blended image corresponds to the value of the first pixelof the image received at 204 multiplied by a weighting factor of 80%combined (e.g., summed together) with the value of the correspondingpixel of the first filtered image multiplied by a weighting factor of20%. In some embodiments, the user may readjust the desired contributionof the first filtered image on the fly to change the visibility of noisein the blended image.

When, responsive to the comparing, the target noise contribution has notbeen satisfied, the first filtered image is filtered to generate asecond filtered image using one or more of the techniques used to filterthe image at 208. Further, a third noise contribution to the secondfiltered image is estimated using one or more of the estimationtechniques used to estimate the second noise contribution at 210, andthe third noise contribution is compared to the target noisecontribution to determine if the additional filtering has caused thenoise contribution to be satisfied. Such a process may be repeated untilthe target noise contribution is satisfied, at which point the mostrecently generated filtered image may be blended with the image receivedat 204 to generate the filtered image.

Referring to FIG. 3, a flow diagram of an example method 300 forfiltering an image yielded from a radiation examination of an object isprovided. For purposes of brevity, features of FIG. 3 that are describedwith respect to FIG. 2 are not described in detail with respect to FIG.3.

The example method 300 begins at 302 when an image is received. Theimage is a post-reconstruction image and may comprise artifacts due tophoton noise, due to electronic noise, and/or due to the process ofreconstructing an image from projection space (e.g., from sine waves).

At 304 in the example method 300, noise in the image is estimated and atarget noise contribution (e.g., a desired percentage reduction innoise) is derived from the noise estimation at 306. In some embodiments,the noise is estimated merely based upon pixels having a value within arange that is expected for tissue (e.g., thus excluding pixelsrepresentative of non-tissue from the estimation).

At 308 in the example method 300, a first filtering technique isperformed on the image to generate an intermediary image. In someembodiments, the first filtering technique is an outlier filteringtechnique, such as a 3D outlier filtering technique that replaces thevalue for outlying pixels (e.g., pixels that deviate from a threshold bya deviation threshold). For example, the 3D outlier filtering techniquemay use a pixel neighborhood around respective pixels to replaceoutliers with a weighted sum and/or mean pixel value of the neighbors.The mean pixel value may include a value of the pixel and/or may excludethis value when computing the mean pixel value.

In some embodiments, the first filtering technique discriminates betweenpixels based upon the value of the pixel (e.g., and thus a feature ofthe object that is likely represented by the pixel). For example, themean pixel value may be applied to some pixels that deviate from themean pixel value by a first value while not applying the mean pixelvalue to other pixels that deviate from the mean pixel value by thefirst value because the threshold for what is considered an outlier maybe different for different pixels. For example, where the pixel has avalue of 200 HU (e.g., and thus likely to be indicative of non-tissue) agreater variance between the pixel and neighboring pixels may be deemedacceptable than would be acceptable if the pixel had a value of 50 HU(e.g., and thus likely to be indicative of tissue), becausecharacteristics of non-tissue are likely to cause greater naturalvariation in pixel values representative of the non-tissue than inpixels representatives of tissue (e.g., which may have little to novariation in density characteristic, z-effective characteristic, etc.).

At 310 in the example method 300, a second filtering technique isperformed on the intermediary image to generate a filtered image. Thesecond technique may comprise a diffusion technique, such as ananisotropic diffusion technique, that diffuses (e.g., distributes) aportion of a pixel to a neighborhood of pixels around the pixel. Asanother example, a wavelet technique may be performed on the image togenerate the filtered image.

In some embodiments, whereas the first filtering technique performed at308 may discriminate between pixels based upon pixel value (e.g., andthus based upon whether the pixel likely represents tissue ornon-tissue), the second filtering technique may treat respective pixelsthe same. Thus, the degree of diffusion for a pixel, for example, maynot be dependent upon whether the pixel is likely to be representativeof non-tissue or tissue. Rather, the degree of diffusion may be basedupon a difference between the value of the pixel relative to the meanpixel value of a set of pixels neighboring the pixel, and desired scopeof diffusion (e.g., across how many neighboring pixels the difference isto be distributed, etc.).

At 312 in the example method 300, noise in the filtered image isestimated. In some embodiments, the noise is estimated merely based uponpixels of the filtered image that correspond to pixels of the imagereceived at 302 that were used to estimate noise in the image (e.g.,such that pixels likely to be representative of non-tissue are excludedfrom the estimate).

At 314 in the example method 300, the estimated noise in the filteredimage is compared to the target noise contribution to determine if thetarget noise contribution has been satisfied. For example, the estimatednoise in the filtered image is compared to the estimated noise in theimage received at 302 to determine if there has been a 20% reduction innoise (e.g., where 20% reduction corresponds to the target noisecontribution). When the estimated noise in the filtered image exceedsthe target noise contribution, and thus the target noise contribution isnot satisfied, the filtered image is transmitted to the second filter,where the second filtering technique is performed on the filtered imageto generate a second filtered image.

In some embodiments, where a portion of the method 300 is iterativelyrepeated by sending the filtered image back to the second filter, one ormore parameters of the second filtering technique are adjusted betweenone or more iterations. By way of example, during a first iteration,when the intermediary image is filtered to generate a first filteredimage, the second filtering technique may apply a level 1 diffusion tothe intermediary image (e.g., where a portion of the difference betweenthe first pixel and the mean pixel value is distributed merely toimmediately adjacent pixels). During a second iteration, when the firstfiltered image is filtered to generate a second filtered image, thesecond filtering technique may apply a level 2 diffusion to the firstfiltered image (e.g., where a portion of the difference between thefirst pixel and the mean pixel value is distributed to pixels separatedfrom the first pixel by no more than 1 pixel). As another example, theamount of the difference between the value of a pixel and the mean pixelvalue that is diffused may differ between iterations. For example,during the first iteration, 10% of the difference between a first pixelof the intermediary image and a mean pixel value for pixels neighboringthe first pixel may be diffused. During a next iteration, 5% of thedifference between a pixel of the first filtered image corresponding tothe first pixel of the intermediary image and a mean pixel value forpixels neighboring the pixel may be diffused. In some embodiments, ascope of diffusion (e.g., a number of pixels across which the differenceis distributed) and/or an extent of diffusion (e.g., percent of thedifference that is distributed) may be variable based upon imagefeatures (e.g., which can be measured based upon image gradient and/or aCT value of respective pixels). By way of example, the scope and/orextent of diffusion for an image depicting mostly organs may bedifferent than the scope and/or extent of diffusion for an imagedepicting mostly bones and/or other non-tissue.

When the estimated noise in the filtered image does not exceed thetarget noise contribution, and thus the target noise contribution issatisfied, the filtered image (e.g., the most recently generatedfiltered image) is blended with the image to generate a blended image at316. The blended image represents a merging of the filtered image withthe image and may be presented to the user for inspection. Moreover, insome embodiments, the user may adjust a contribution of the filteredimage to the blended image to alter features of the blended image. Forexample, a first blended image may be generated with a 20% contributionof the filtered image and an 80% contribution of the original image. Ifthe user does not like the appearance of the first blended image (e.g.,because edges have been too smoothed by the filtering, etc.) the usermay request that a second blended image be generated having lesscontribution from the filtered image. For example, the second blendedimage may be generated with a 10% contribution of the filtered image anda 90% contribution of the original image.

Still another embodiment involves a computer-readable medium comprisingprocessor-executable instructions configured to implement one or more ofthe techniques presented herein. An example computer-readable mediumthat may be devised in these ways is illustrated in FIG. 4, wherein theimplementation 400 comprises a computer-readable medium 402 (e.g., aflash drive, CD-R, DVD-R, application-specific integrated circuit(ASIC), field-programmable gate array (FPGA), a platter of a hard diskdrive, etc.), on which is encoded computer-readable data 404. Thiscomputer-readable data 404 in turn comprises a set ofprocessor-executable instructions 406 configured to operate according toone or more of the principles set forth herein. In one such embodiment400, the processor-executable instructions 406 may be configured toperform a method 408 when executed via a processing unit, such as atleast some of the example method 200 of FIG. 2 and/or at least some ofexample method 300 of FIG. 3. In another such embodiment, theprocessor-executable instructions 406 may be configured to implement asystem, such as at least some of the example radiation imaging system100 of FIG. 1. Many such computer-readable media may be devised by thoseof ordinary skill in the art that are configured to operate inaccordance with one or more of the techniques presented herein. Althoughthe subject matter has been described in language specific to structuralfeatures and/or methodological acts, it is to be understood that thesubject matter defined in the appended claims is not necessarily limitedto the specific features or acts described above. Rather, the specificfeatures and acts described above are disclosed as example forms ofimplementing the claims.

Although the subject matter has been described in language specific tostructural features or methodological acts, it is to be understood thatthe subject matter of the appended claims is not necessarily limited tothe specific features or acts described above. Rather, the specificfeatures and acts described above are disclosed as embodiment forms ofimplementing at least some of the claims.

Various operations of embodiments are provided herein. The order inwhich some or all of the operations are described should not beconstrued to imply that these operations are necessarily orderdependent. Alternative ordering will be appreciated given the benefit ofthis description. Further, it will be understood that not all operationsare necessarily present in each embodiment provided herein. Also, itwill be understood that not all operations are necessary in someembodiments.

Moreover, “exemplary” is used herein to mean serving as an example,instance, illustration, etc., and not necessarily as advantageous. Asused in this application, “or” is intended to mean an inclusive “or”rather than an exclusive “or”. In addition, “a” and “an” as used in thisapplication are generally be construed to mean “one or more” unlessspecified otherwise or clear from context to be directed to a singularform. Also, at least one of A and B and/or the like generally means A orB or both A and B. Furthermore, to the extent that “includes”, “having”,“has”, “with”, or variants thereof are used, such terms are intended tobe inclusive in a manner similar to the term “comprising”. The claimedsubject matter may be implemented as a method, apparatus, or article ofmanufacture (e.g., as software, firmware, hardware, or any combinationthereof).

As used in this application, the terms “component,” “module,” “system”,“interface”, and the like are generally intended to refer to acomputer-related entity, either hardware, a combination of hardware andsoftware, software, or software in execution. For example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution, a program,and/or a computer. By way of illustration, both an application runningon a controller and the controller can be a component. One or morecomponents may reside within a process and/or thread of execution and acomponent may be localized on one computer and/or distributed betweentwo or more computers.

Furthermore, the claimed subject matter may be implemented as a method,apparatus, or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware, or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device, carrier, or media. Of course, those skilled inthe art will recognize many modifications may be made to thisconfiguration without departing from the scope or spirit of the claimedsubject matter.

Further, unless specified otherwise, “first,” “second,” and/or the likeare not intended to imply a temporal aspect, a spatial aspect, anordering, etc. Rather, such terms are merely used as identifiers, names,etc. for features, elements, items, etc. For example, a first channeland a second channel generally corresponds to channel A and channel B ortwo different or two identical channels or the same channel.

Although the disclosure has been shown and described with respect to oneor more implementations, equivalent alterations and modifications willoccur to others skilled in the art based upon a reading andunderstanding of this specification and the annexed drawings. Thedisclosure includes all such modifications and alterations and islimited only by the scope of the following claims. In particular regardto the various functions performed by the above described components(e.g., elements, resources, etc.), the terms used to describe suchcomponents are intended to correspond, unless otherwise indicated, toany component which performs the specified function of the describedcomponent (e.g., that is functionally equivalent), even though notstructurally equivalent to the disclosed structure. In addition, while aparticular feature of the disclosure may have been disclosed withrespect to only one of several implementations, such feature may becombined with one or more other features of the other implementations asmay be desired and advantageous for any given or particular application.

What is claimed is:
 1. A method for processing images yielded from anexamination via radiation, comprising: receiving an image of an objectthat has been exposed to radiation, the image generated based upon aninteraction between the radiation and the object; estimating a firstnoise contribution to the image to derive a target noise contribution;filtering the image to generate a first filtered image; estimating asecond noise contribution to the first filtered image; and comparing thetarget noise contribution to the second noise contribution to determinewhether the target noise contribution has been satisfied by thefiltering.
 2. The method of claim 1, comprising: combining the firstfiltered image with the image when, responsive to the comparing, thetarget noise contribution has been satisfied by the filtering.
 3. Themethod of claim 2, the combining comprising: applying a first weight tothe first filtered image to generate a first weighted image; applying asecond weight to the image to generate a second weighted image, thesecond weight different than the first weight; and merging the firstweighted image with the second weighted image.
 4. The method of claim 3,comprising determining at least one of the first weight or the secondweight based upon user input.
 5. The method of claim 1, the filteringcomprising distributing a portion of a signal corresponding to a firstpixel of the image to a set of one or more pixels neighboring the firstpixel.
 6. The method of claim 1, the filtering comprising: computing amean pixel value for a set of one or more pixels neighboring a firstpixel; comparing the mean pixel value to a value of the first pixel todetermine a deviation value; and applying the mean pixel value to thefirst pixel when the deviation value exceeds a deviation threshold. 7.The method of claim 6, the filtering comprising: determining thedeviation threshold based upon the value of the first pixel.
 8. Themethod of claim 6, the value of the first pixel corresponding to acomputed tomography (CT) value of the first pixel.
 9. The method ofclaim 6, the filtering comprising: setting the deviation threshold to afirst deviation threshold value when the value of the first pixel iswithin a first pixel value range; and setting the deviation threshold toa second deviation threshold value when the value of the first pixel iswithin a second pixel value range, the second pixel value rangedifferent than the first pixel value range and the second deviationthreshold different than the first deviation threshold.
 10. The methodof claim 1, wherein when, responsive to the comparing, the target noisecontribution has not been satisfied by the filtering, the methodcomprises: filtering the first filtered image to generate a secondfiltered image; estimating a third noise contribution to the secondfiltered image; and comparing the target noise contribution to the thirdnoise contribution to determine whether the target noise contributionhas been satisfied by the filtering the first filtered image.
 11. Themethod of claim 10, comprising: combining the second filtered image withthe image when, responsive to the comparing the target noisecontribution to the third noise contribution, the target noisecontribution has been satisfied by the filtering the first filteredimage.
 12. The method of claim 1, wherein the image is athree-dimensional image.
 13. The method of claim 1, at least one of: theestimating a first noise contribution comprising computing a firstspatial derivative of a pixel value of a pixel of the image, or theestimating a second noise contribution comprising computing a secondspatial derivative of a pixel value of a pixel of the first filteredimage.
 14. The method of claim 13, at least one of: the computing afirst spatial derivative comprising computing a derivative of the pixelvalue of the pixel of the image in at least two dimensions, or thecomputing a second spatial derivative comprising computing a derivativeof the pixel value of the pixel of the first filtered image in at leasttwo dimensions.
 15. A computer-readable medium comprisingprocessor-executable instructions that when executed perform a method,comprising: estimating a first noise contribution to an image yieldedfrom a radiation examination of an object to derive a target noisecontribution; filtering the image to generate a first filtered image;estimating a second noise contribution to the first filtered image;comparing the target noise contribution to the second noise contributionto determine whether the target noise contribution has been satisfied bythe filtering; and combining the first filtered image with the imagewhen, responsive to the comparing, the target noise contribution hasbeen satisfied by the filtering.
 16. The computer-readable medium ofclaim 15, the combining comprising: applying a first weight to the firstfiltered image to generate a first weighted image; applying a secondweight to the image to generate a second weighted image, the secondweight different than the first weight; and merging the first weightedimage with the second weighted image.
 17. The computer-readable mediumof claim 15, the image comprising a three-dimensional (3D) image. 18.The computer-readable medium of claim 15, the filtering comprisingdistributing a portion of a signal corresponding to a first pixel of theimage to a set of one or more pixels neighboring the first pixel. 19.The computer-readable medium of claim 15, the filtering comprising:computing a mean pixel value for a set of one or more pixels neighboringa first pixel; comparing the mean pixel value to a value of the firstpixel to determine a deviation value; and applying the mean pixel valueto the first pixel when the deviation value exceeds a deviationthreshold.
 20. A radiation imaging system, comprising: a radiationsource configured to expose an object under examination to radiation; adetector array configured to produce one or more signals based upondetecting at least some of the radiation that traverses the object; animage reconstruction component configured to reconstruct an image basedupon the one or more signals; and a noise reduction component configuredto: estimate a first noise contribution to the image to derive a targetnoise contribution; filter the image to generate a first filtered image;estimate a second noise contribution to the first filtered image;compare the target noise contribution to the second noise contributionto determine whether the target noise contribution has been satisfied bythe filtering; and combine the first filtered image with the image when,responsive to the comparing, the target noise contribution has beensatisfied by the filtering.